Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

BACKGROUND: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. OBJECTIVE: The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors. METHODS: Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors. RESULTS: The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. CONCLUSIONS: Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. TRIAL REGISTRATION: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-050785.

Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet.

Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1,000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised gait detection method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults.

Effectiveness of autologous haematopoietic stem cell transplantation versus natalizumab in progressive multiple sclerosis.

BACKGROUND: Natalizumab was not shown to modify disability in progressive multiple sclerosis (MS). This matched observational study compared the effectiveness of autologous haematopoietic stem cell transplantation (AHSCT) with natalizumab in progressive MS. METHODS: Patients with primary/secondary progressive MS from seven AHSCT MS centres and the MSBase registry, treated with AHSCT or natalizumab, were matched on a propensity score derived from sex, age, Expanded Disability Status Scale (EDSS), number of relapses 12/24 months before baseline, time from MS onset, the most effective prior therapy and country. The pairwise-censored groups were compared on hazards of 6-month confirmed EDSS worsening and improvement, relapses and annualised relapse rates (ARRs), using Andersen-Gill proportional hazards models and conditional negative binomial model. RESULTS: 39 patients treated with AHSCT (37 with secondary progressive MS, mean age 37 years, EDSS 5.7, 28% with recent disability progression, ARR 0.54 during the preceding year) were matched with 65 patients treated with natalizumab. The study found no evidence for difference in hazards of confirmed EDSS worsening (HR 1.49, 95% CI 0.70 to 3.14) and improvement (HR 1.50, 95% CI 0.22 to 10.29) between AHSCT and natalizumab over up to 4 years. The relapse activity was also similar while treated with AHSCT and natalizumab (ARR: mean±SD 0.08±0.28 vs 0.08±0.25; HR 1.05, 95% CI 0.39 to 2.82). In the AHSCT group, 3 patients experienced febrile neutropenia during mobilisation, 9 patients experienced serum sickness, 6 patients required intensive care unit admission and 36 patients experienced complications after discharge. No treatment-related deaths were reported. CONCLUSION: This study does not support the use of AHSCT to control disability in progressive MS with advanced disability and low relapse activity.

Efficacy and safety of autologous haematopoietic stem cell transplantation versus alemtuzumab, ocrelizumab, ofatumumab or cladribine in relapsing remitting multiple sclerosis (StarMS): protocol for a randomised controlled trial.

INTRODUCTION: Autologous haematopoietic stem cell transplantation (aHSCT) is increasingly used as treatment for patients with active multiple sclerosis (MS), typically after failure of disease-modifying therapies (DMTs). A recent phase III trial, 'Multiple Sclerosis International Stem Cell Transplant, MIST', showed that aHSCT resulted in prolonged time to disability progression compared with DMTs in patients with relapsing remitting MS (RRMS). However, the MIST trial did not include many of the current high-efficacy DMTs (alemtuzumab, ocrelizumab, ofatumumab or cladribine) in use in the UK within the control arm, which are now offered to patients with rapidly evolving severe MS (RES-MS) who are treatment naïve. There remain, therefore, unanswered questions about the relative efficacy and safety of aHSCT over these high-efficacy DMTs in these patient groups. The StarMS trial (Autologous Stem Cell Transplantation versus Alemtuzumab, Ocrelizumab, Ofatumumab or Cladribine in Relapsing Remitting Multiple Sclerosis) will assess the efficacy, safety and long-term impact of aHSCT compared with high-efficacy DMTs in patients with highly active RRMS despite the use of standard DMTs or in patients with treatment naïve RES-MS. METHODS AND ANALYSIS: StarMS is a multicentre parallel-group rater-blinded randomised controlled trial with two arms. A total of 198 participants will be recruited from 19 regional neurology secondary care centres in the UK. Participants will be randomly allocated to the aHSCT arm or DMT arm in a 1:1 ratio. Participants will remain in the study for 2 years with follow-up visits at 3, 6, 9, 12, 18 and 24 months postrandomisation. The primary outcome is the proportion of patients who achieve 'no evidence of disease activity' during the 2-year postrandomisation follow-up period in an intention to treat analysis. Secondary outcomes include efficacy, safety, cost-effectiveness and immune reconstitution of aHSCT and the four high-efficacy DMTs. ETHICS AND DISSEMINATION: The study was approved by the Yorkshire and Humber-Leeds West Research Ethics Committee (20/YH/0061). Participants will provide written informed consent prior to any study specific procedures. The study results will be submitted to a peer-reviewed journal and abstracts will be submitted to relevant national and international conferences. TRIAL REGISTRATION NUMBER: ISRCTN88667898.

Innovative cellular therapies for autoimmune diseases: expert-based position statement and clinical practice recommendations from the EBMT practice harmonization and guidelines committee.

Autoimmune diseases (ADs) are characterized by loss of immune tolerance, high chronicity, with substantial morbidity and mortality, despite conventional immunosuppression (IS) or targeted disease modifying therapies (DMTs), which usually require repeated administration. Recently, novel cellular therapies (CT), including mesenchymal stromal cells (MSC), Chimeric Antigen Receptors T cells (CART) and regulatory T cells (Tregs), have been successfully adopted in ADs. An international expert panel of the European Society for Blood and Marrow Transplantation and the International Society for the Cell and Gene Therapy, reviewed all available evidence, based on the current literature and expert practices, on use of MSC, CART and Tregs, in AD patients with rheumatological, neurological, and gastroenterological indications. Expert-based consensus and recommendations for best practice and quality of patient care were developed to support clinicians, scientists, and their multidisciplinary teams, as well as patients and care providers and will be regularly updated.

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device.

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.

Correlates and trajectories of relapses in relapsing-remitting multiple sclerosis.

BACKGROUND AND AIMS: In people with relapsing-remitting multiple sclerosis (pwRRMS), data from studies on non-pharmacological factors which may influence relapse risk, other than age, are inconsistent. There is a reduced risk of relapses with increasing age, but little is known about other trajectories in real-world MS care. METHODS: We studied longitudinal questionnaire data from 3885 pwRRMS, covering smoking, comorbidities, disease-modifying therapy (DMT), and patient-reported outcome measures, as well as relapses during the past year. We undertook Rasch analysis, group-based trajectory modelling, and multilevel negative binomial regression. RESULTS: The regression cohort of 6285 data sets from pwRRMS over time showed that being a current smoker was associated with 43.9% greater relapse risk; having 3 or more comorbidities increased risk and increasing age reduced risk. Those diagnosed within the last 2 years showed two distinct trajectories, both reducing in relapse frequency but 25.8% started with a higher rate and took 4 years to reduce to the rate of the second group. In the cohort with at least three data points completed, there were three groups: 73.7% followed a low stable relapse rate, 21.6% started from a higher rate and decreased, and 4.7% had an increasing then decreasing pattern. These different trajectory groups showed significant differences in fatigue, neuropathic pain, disability, health status, quality of life, self-efficacy, and DMT use. CONCLUSIONS: These results provide additional evidence for supporting pwRRMS to stop smoking and underline the importance of timely DMT decisions and treatment initiation soon after diagnosis with RRMS.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.

Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods: Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion: The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

Psychometric Properties of LUN-MS: A New Questionnaire to Identify the Unmet Needs of People With Multiple Sclerosis.

Background: We developed a 29-item Questionnaire, Long-term Unmet Needs in MS (LUN-MS) to identify the unmet needs of people with multiple sclerosis (pwMS). Objective: To assess acceptability, test-retest reliability, internal consistency, and validity of the LUN-MS. Methods: Participants completed the LUN-MS and MSIS-29 twice, four weeks apart. Acceptability was assessed by looking at the response rate in each time point. Reliability was calculated by comparing the response during the two time points using Cohen's weighted kappa. Using principal component analysis, the dimensionality of the questionnaire's items was reduced, to five domains and the internal consistency of each domain was assessed using Cronbach's alpha. Concurrent validity was tested by comparing the total LUN-MS score against MSIS-29 and EQ-5D-3L using Pearson's product-moment correlation coefficient. Results: Among 88 participants, rate of completion at time points-1 and 2 was 96 and 80% respectively. Test-retest reliability for individual items was between fair to near-perfect (weighted Cohen's kappa 0.39-0.81). The unmet needs could be divided into five internally consistent domains (Cronbach's alpha 0.83-0.74): neuropsychological, ambulation, physical, interpersonal relationship and informational. Concurrent validity with MSIS-29 (r = 0.705, P < .001) and EQ-5D-3L (r = 0.617, P < .001) were good. Conclusion: LUN-MS is a reliable, valid, and acceptable tool to identify the unmet needs of pwMS.

Measuring disability in multiple sclerosis: the WHODAS 2.0.

INTRODUCTION: Reliable measurement of disability in multiple sclerosis (MS) using a comprehensive, patient self-reported scale, such as the World Health Organization Disability Assessment Schedule (WHODAS) 2.0, would be of clinical and research benefit. METHODS: In the Trajectories of Outcome in Neurological Conditions-MS study, WHODAS 2.0 (WHODAS-36 items for working, WHODAS-32 items if not working, WHODAS-12 items short-form) was examined using Rasch analysis in 5809 people with MS. RESULTS: The 36- and 32-item parallel forms, and the cognitive and physical domains, showed reliability consistent with individual or group use. The 12-item short-form is valid for group use only. Interval level measurement for parametric statistics can be derived from all three scales which showed medium to strong effect sizes for discrimination across characteristics such as age, subtype, and disease duration. Smallest detectable difference for each scale was < 6 on the standardised metric of 0-100 so < 6% of the total range. There was no substantial differential item functioning (DIF) by age, gender, education, working full/part-time, or disease duration; the finding of no DIF for time or sample supports the use of WHODAS 2.0 for longitudinal studies, with the 36- and 32-item versions and the physical and cognitive domains valid for individual patient follow-up. CONCLUSIONS: Disability in MS can be comprehensively measured at interval level by the WHODAS 2.0, and validly monitored over time. Routine use of this self-reported measure in clinical and research practice would give valuable information on the trajectories of disability of individuals and groups.

Comparative Effectiveness of Autologous Hematopoietic Stem Cell Transplant vs Fingolimod, Natalizumab, and Ocrelizumab in Highly Active Relapsing-Remitting Multiple Sclerosis.

Importance: Autologous hematopoietic stem cell transplant (AHSCT) is available for treatment of highly active multiple sclerosis (MS). Objective: To compare the effectiveness of AHSCT vs fingolimod, natalizumab, and ocrelizumab in relapsing-remitting MS by emulating pairwise trials. Design, Setting, and Participants: This comparative treatment effectiveness study included 6 specialist MS centers with AHSCT programs and international MSBase registry between 2006 and 2021. The study included patients with relapsing-remitting MS treated with AHSCT, fingolimod, natalizumab, or ocrelizumab with 2 or more years study follow-up including 2 or more disability assessments. Patients were matched on a propensity score derived from clinical and demographic characteristics. Exposure: AHSCT vs fingolimod, natalizumab, or ocrelizumab. Main outcomes: Pairwise-censored groups were compared on annualized relapse rates (ARR) and freedom from relapses and 6-month confirmed Expanded Disability Status Scale (EDSS) score worsening and improvement. Results: Of 4915 individuals, 167 were treated with AHSCT; 2558, fingolimod; 1490, natalizumab; and 700, ocrelizumab. The prematch AHSCT cohort was younger and with greater disability than the fingolimod, natalizumab, and ocrelizumab cohorts; the matched groups were closely aligned. The proportion of women ranged from 65% to 70%, and the mean (SD) age ranged from 35.3 (9.4) to 37.1 (10.6) years. The mean (SD) disease duration ranged from 7.9 (5.6) to 8.7 (5.4) years, EDSS score ranged from 3.5 (1.6) to 3.9 (1.9), and frequency of relapses ranged from 0.77 (0.94) to 0.86 (0.89) in the preceding year. Compared with the fingolimod group (769 [30.0%]), AHSCT (144 [86.2%]) was associated with fewer relapses (ARR: mean [SD], 0.09 [0.30] vs 0.20 [0.44]), similar risk of disability worsening (hazard ratio [HR], 1.70; 95% CI, 0.91-3.17), and higher chance of disability improvement (HR, 2.70; 95% CI, 1.71-4.26) over 5 years. Compared with natalizumab (730 [49.0%]), AHSCT (146 [87.4%]) was associated with marginally lower ARR (mean [SD], 0.08 [0.31] vs 0.10 [0.34]), similar risk of disability worsening (HR, 1.06; 95% CI, 0.54-2.09), and higher chance of disability improvement (HR, 2.68; 95% CI, 1.72-4.18) over 5 years. AHSCT (110 [65.9%]) and ocrelizumab (343 [49.0%]) were associated with similar ARR (mean [SD], 0.09 [0.34] vs 0.06 [0.32]), disability worsening (HR, 1.77; 95% CI, 0.61-5.08), and disability improvement (HR, 1.37; 95% CI, 0.66-2.82) over 3 years. AHSCT-related mortality occurred in 1 of 159 patients (0.6%). Conclusion: In this study, the association of AHSCT with preventing relapses and facilitating recovery from disability was considerably superior to fingolimod and marginally superior to natalizumab. This study did not find evidence for difference in the effectiveness of AHSCT and ocrelizumab over a shorter available follow-up time.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

BACKGROUND: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. METHODS: Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. RESULTS: We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. CONCLUSIONS: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.

Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Acceptability of wearable devices for measuring mobility remotely: Observations from the Mobilise-D technical validation study.

Background: This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health. Methods: Participants (N = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire. A subset of participants (n = 36) also completed semi-structured interviews to further determine device acceptability and to explore their opinions of the use of digital tools to monitor their health. Questionnaire results were analysed descriptively and interviews using a content analysis. Results: The device was considered both comfortable (median CRS (IQR; min-max) = 0.0 (0.0; 0-20) on a scale from 0-20 where lower scores signify better comfort) and acceptable (5.0 (0.5; 3.0-5.0) on a scale from 1-5 where higher scores signify better acceptability). Interviews showed it was easy to use, did not interfere with daily activities, and was comfortable. The following themes emerged from participants' as being important to digital technology: altered expectations for themselves, the use of technology, trust, and communication with healthcare professionals. Conclusions: Digital tools may bridge existing communication gaps between patients and clinicians and participants are open to this. This work indicates that waist-worn devices are supported, but further work with patient advisors should be undertaken to understand some of the key issues highlighted. This will form part of the ongoing work of the Mobilise-D consortium.

Listening to the patients' voice: a conceptual framework of the walking experience.

BACKGROUND: walking is crucial for an active and healthy ageing, but the perspectives of individuals living with walking impairment are still poorly understood. OBJECTIVES: to identify and synthesise evidence describing walking as experienced by adults living with mobility-impairing health conditions and to propose an empirical conceptual framework of walking experience. METHODS: we performed a systematic review and meta-ethnography of qualitative evidence, searching seven electronic databases for records that explored personal experiences of walking in individuals living with conditions of diverse aetiology. Conditions included Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture, heart failure, frailty and sarcopenia. Data were extracted, critically appraised using the NICE quality checklist and synthesised using standardised best practices. RESULTS: from 2,552 unique records, 117 were eligible. Walking experience was similar across conditions and described by seven themes: (i) becoming aware of the personal walking experience, (ii) the walking experience as a link between individuals' activities and sense of self, (iii) the physical walking experience, (iv) the mental and emotional walking experience, (v) the social walking experience, (vi) the context of the walking experience and (vii) behavioural and attitudinal adaptations resulting from the walking experience. We propose a novel conceptual framework that visually represents the walking experience, informed by the interplay between these themes. CONCLUSION: a multi-faceted and dynamic experience of walking was common across health conditions. Our conceptual framework of the walking experience provides a novel theoretical structure for patient-centred clinical practice, research and public health.

Autologous haematopoietic stem cell transplantation for immune-mediated neurological diseases: what, how, who and why?

In carefully selected patients, autologous haematopoietic stem cell transplantation (HSCT) is a safe, highly effective and cost-saving treatment modality for treatment-resistant, and potentially treatment-naïve, immune-mediated neurological disorders. Although the evidence base has been growing in the last decade, limited understanding has led to confusion, mistrust and increasing use of health tourism. In this article, we discuss what autologous HSCT is, which immune-mediated conditions can be treated with it, how to select patients, what are the expected outcomes and potential adverse effects, and how cost-effective this treatment is.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

BACKGROUND: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. METHODS: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. RESULTS: The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. CONCLUSIONS: The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. TRIAL REGISTRATION: ISRCTN-12246987.

On the use of wearable sensors as mobility biomarkers in the marketing authorization of new drugs: A regulatory perspective.

The loss of mobility is a common trait in multiple health conditions (e.g., Parkinson's disease) and is associated with reduced quality of life. In this context, being able to monitor mobility in the real world, is important. Until recently, the technology was not mature enough for this; but today, miniaturized sensors and novel algorithms promise to monitor mobility accurately and continuously in the real world, also in pathological populations. However, before any such methodology can be employed to support the development and testing of new drugs in clinical trials, they need to be qualified by the competent regulatory agencies (e.g., European Medicines Agency). Nonetheless, to date, only very narrow scoped requests for regulatory qualification were successful. In this work, the Mobilise-D Consortium shares its positive experience with the European regulator, summarizing the two requests for Qualification Advice for the Mobilise-D methodologies submitted in October 2019 and June 2020, as well as the feedback received, which resulted in two Letters of Support publicly available for consultation on the website of the European Medicines Agency. Leveraging on this experience, we hereby propose a refined qualification strategy for the use of digital mobility outcome (DMO) measures as monitoring biomarkers for mobility in drug trials.

Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement-the Mobilise-D study protocol.

BACKGROUND: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. METHODS/DESIGN: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. DISCUSSION: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. TRIAL REGISTRATION: ISRCTN12051706.